Non-Invasive Method for In-Service Induction Motor Efficiency Estimation Based on Sound Acquisition

Silva, Júlio César da; Lima, Thyago Leite de Vasconcelos; Júnior, José Anselmo de Lucena; Lyra, Gabriela Jordão; Souto, Filipe Vidal; Pimentel, Hugo de Souza; Belo, Francisco Antônio; Filho, Abel Cavalcante Lima

doi:10.3390/app10113757

Cited by 3 publications

(1 citation statement)

References 26 publications

(39 reference statements)

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“…Firstly, machine faults are identified in the signal spectrum through peaks at characteristic frequencies, as each component or fault emits a unique frequency vibration. The intensity of the characteristic harmonic indicates the presence or severity of such a fault [26].…”

Section: Scenario 3: Data Collection From the Engine With Failed Bear...mentioning

confidence: 99%

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Araujo,

Bissiriou,

Villanueva

et al. 2024

Preprint

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Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through accurate diagnosis and classification of faults in three-phase induction motors, using artificial intelligence techniques, by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network, using the scanning method with multiple training and validation iterations, with the introduction of new data. The results of these tests showed that the network had excellent generalization in all the situations evaluated, achieving the following accuracy rates: Motor without fault = 94.2%, Unbalance fault = 95%, Bearings with fault =98% and Stator with fault = 95%.

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Section: Scenario 3: Data Collection From the Engine With Failed Bear...mentioning

confidence: 99%

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Araujo,

Bissiriou,

Villanueva

et al. 2024

Preprint

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Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone

et al. 2022

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Induction motors are key pieces of equipment in today’s society, powering a variety of industrial drives and home appliances. The induction motor speed is often used to monitor the performance of all kinds of industrial drives. For example, in the industrial field, the motor speed is very often used to determine the efficiency and mechanical load of motors. In this work, a new simple, low-cost, and nonintrusive procedure is proposed for infield measurement of induction motors speed, which is based on the spectral analysis of the vibration signal of the motors. The motor vibration signal is first acquired using the accelerometers integrated into a basic phone. The acquired signal is then treated by a MATLAB-based algorithm, which can determine the motor speed by identifying the mechanical frequency of the rotor shaft from the harmonic content of the vibration signal. In this way, it is shown that the mechanical frequency corresponding to the speed of rotation of the motors can be acquired by means of the embedded accelerometers of a common smartphone, avoiding the acquisition and installation of external accelerometers. To the authors’ knowledge, this could be the first time that a smartphone has been proposed as a practical means of measuring the speed of a motor by analysing its vibration. Experimental results from an extensive set of tests, including the supply of the motor from a frequency converter, show that the speed can always be measured with a relative error of less than 0.15%.

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Monitoring and Diagnosing Faults in Induction Motors’ Three-Phase Systems Using NARX Neural Network

Araújo,

Bissiriou,

Villanueva

et al. 2024

Energies

View full text Add to dashboard Cite

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = 94.2%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.

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Non-Invasive Method for In-Service Induction Motor Efficiency Estimation Based on Sound Acquisition

Cited by 3 publications

References 26 publications

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Monitoring and Diagnosing Faults in Three-Phase Induction Motors Using Artificial Intelligence Techniques

Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone

Monitoring and Diagnosing Faults in Induction Motors’ Three-Phase Systems Using NARX Neural Network

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